System and method for determining thresholds of range of values used to allocate patients to a treatment level of a treatment program

ABSTRACT

There is provided a system for determining a threshold value or range of values for results of a test used to assign patients to a particular level of treatment for a clinical condition, the system comprising one or more databases storing historical information on a plurality of patients having the clinical condition, the information including values for the test performed on the patients, information on the treatment level for the clinical condition provided to the patients, there being a plurality of available treatment levels for the clinical condition, information on the outcome of the clinical condition for the patients and information on the cost associated with providing each of the treatment levels to the patients; and a processing module in communication with said one or more databases, the processing module determining a threshold value or range of values for results of the test from the information in said one or more databases, with the threshold value or range of values indicating the most cost effective treatment level for a given test result.

TECHNICAL FIELD OF THE INVENTION

The invention relates to treatment programs for chronic diseases thathave a number of different intensity levels of treatment available to apatient, and in particular relates to a system and method fordetermining threshold values or a range of values for a test used toassess the current condition of the patient, the threshold values orrange of values being used to allocate patients to an appropriateintensity level of treatment for the current status of the disease forthe patient.

BACKGROUND TO THE INVENTION

With the average age of the population increasing, the number of peoplewith chronic diseases is also increasing. There is a lack of healthcarestaff that are able to manage patients with chronic diseases and costsof treatments are rising, which has stimulated interest in developingchronic disease management (CDM) programs to try and avoid costlyhospital readmissions. One example of a CDM program is hometelemonitoring of patients with heart failure.

Disease management programs can follow a ‘tiered’ approach, with eachtier or level of service corresponding to a particular “intensity” ofmonitoring, clinical attention and/or intervention, with the tier orlevel allocated being selected based on the severity level of thedisease and, perhaps, the personal preferences of the patient. Dependingon the medical condition, the different tiers can comprise differentamounts or doses of a particular medication, different types ofmedication, different hospital- or home-administered tests and/ordifferent (or some) surgical interventions. Generally, the more‘intense’ the tier of service in the disease management program, thehigher the financial cost of providing that service. As a result,certain programs or tiers of service may only become cost-effective at acertain level of disease severity.

A system and method for performing a cost-utility analysis ofpharmaceutical interventions has been previously described in US2007/0179809 A1. In this system, a patient perceived value, a utilityvalue, an objective value and clinical trial data for variouspharmaceutical interventions is compared to derive a gain per dollarexpended for each pharmaceutical intervention, so that the mosteffective pharmaceutical intervention for the money expended can bechosen.

For some chronic diseases, the severity level of the disease can beobjectively quantified using a test pertaining to the chronic diseaseperformed in a laboratory or by equipment the patient can have at home.For example, N-terminal prohormone brain natriuretic peptide (NTproBNP)levels are a quantitative measure of the acuity level in heart failurepatients (“NTproBNP and the diagnosis of heart failure: a pooledanalysis of three European epidemiological studies”, European Journal ofHeart Failure, 6 (2004), 269-273 by McDonagh, T. A., Holmer, S.,Raymond, I., Luchner, A., Hilderbrant, P. and Dargie, H. J.). The testfor the NTproBNP level is generally performed in a laboratory or in aclinical setting to determine the prognosis for the patient.

SUMMARY OF THE INVENTION

It is an object of the current invention to be able to use the valuesobtained using these laboratory, clinical or home tests to aid thepatient and/or responsible care giver to select an appropriate intensitylevel of a disease management or treatment program, once the disease hasbeen diagnosed by a qualified physician.

It is another object of the invention to provide a system and methodthat can determine cut-off or threshold values for the test or testswhich can be used to indicate the tier of service of the diseasemanagement or treatment program that is most cost-effective for thepatient.

According to a first aspect of the invention, there is provided a systemfor determining a threshold value or range of values for results of atest used to assign patients to a particular level of treatment for aclinical condition, the system comprising one or more databases storinghistorical information on a plurality of patients having the clinicalcondition, the information including values for the test performed onthe patients, information on the treatment level for the clinicalcondition provided to the patients, there being a plurality of availabletreatment levels for the clinical condition, information on the outcomeof the clinical condition for the patients and information on the costassociated with providing each of the treatment levels to the patients;and a processing module in communication with said one or moredatabases, the processing module determining a threshold value or rangeof values for results of the test from the information in said one ormore databases, with the threshold value or range of values indicatingthe most cost effective treatment level for a given test result.

In some embodiments, the processing module is configured to determine athreshold value or range of values for results of the test from theinformation by determining a relationship between values for the testand the health outcome of the clinical condition for each treatmentlevel. The processing module can be configured to determine therelationship through statistical analysis of the information stored inthe one or more databases.

In some embodiments, the processing module is configured to determine athreshold value or range of values for results of the test from theinformation by determining a relationship between values for the testand the monetary cost outcome for each treatment level. The processingmodule can be configured to determine the relationship throughstatistical analysis of the information stored in the one or moredatabases.

In some embodiments, the processing module is configured to determine athreshold value or range of values for results of the test from theinformation by determining a relationship between net health benefitsand threshold values or ranges of values. The net health benefits arepreferably the accumulated effect of said treatment level over apredefined time period minus the total accumulated monetary cost dividedby willingness to pay, the willingness to pay being the monetaryequivalent to a quality-adjusted life year.

The processing module can be configured to determine the relationshipbetween net health benefits and threshold values or ranges of values bysolving a cost-effectiveness model for each treatment level to determinenet health benefits for the plurality of patients. In some embodiments,the cost-effectiveness model comprises a Markov model, differentialequations or difference equations.

In some embodiments, the processing module is further configured todetermine a threshold value or range of values for results of the testfrom the information by determining the threshold value or range ofvalues that maximize the net health benefits; and setting the thresholdvalue or range of values as the threshold value or range of values thatmaximize the net health benefits.

The processing module can be configured to determine a plurality ofestimates of the threshold value or range of values and to solve thecost effectiveness model for each of the plurality of estimates.

Furthermore, the processing module can be configured to determine athreshold value or range of values for results of the test from theinformation by determining a relationship between values for the testand the amount of patients having values for the test at or below thatvalue.

The processing module can additionally be configured to determine athreshold value or range of values for results of the test forrespective groups of patients in the plurality of patients, wherein therespective groups of patients differ from each other in one or more ofage, gender, clinical condition, hospital and geographical area.

In some embodiments, the processing module can be further configured toreceive a result for the test for a patient, compare the result of thetest to the determined threshold value or range of values, and to outputa recommended treatment level for the patient based on the result of thecomparison.

According to a second aspect of the invention, there is provided amethod of determining a threshold value or range of values for resultsof a test used to assign patients to a particular level of treatment fora clinical condition, the method comprising obtaining historicalinformation on a plurality of patients having the clinical condition,the information including values for the test performed on the patients,information on the treatment level for the clinical condition providedto the patients, there being a plurality of available treatment levelsfor the clinical condition, and information on the outcome of theclinical condition for the patients; obtaining information on the costassociated with providing each of the treatment levels to the patients;processing the information to determine a threshold value or range ofvalues for results of the test, the threshold value or range of valuesindicating the most cost effective treatment level for a given testresult.

In some embodiments, the step of processing the information to determinea threshold value or range of values for results of the test comprisesdetermining a relationship between values for the test and the healthoutcome of the clinical condition for each treatment level. Preferably,the step of determining the relationship comprises statisticallyanalyzing the obtained information.

In some embodiments, the step of processing the information to determinea threshold value or range of values for results of the test comprisesdetermining a relationship between values for the test and the monetarycost outcome for each treatment level. Preferably, the step ofdetermining the relationship comprises statistically analyzing theobtained information.

In some embodiments, the step of processing the information to determinea threshold value or range of values for results of the test comprisesdetermining a relationship between net health benefits and thresholdvalues or ranges of values. Preferably, the net health benefits are theaccumulated effect of said treatment level over a predefined time periodminus the total accumulated monetary cost divided by willingness to pay,the willingness to pay being the monetary equivalent to aquality-adjusted life year.

The step of processing the information to determine the relationshipbetween net health benefits and threshold values or ranges of values cancomprise solving a cost-effectiveness model for each treatment level todetermine net health benefits for the plurality of patients. Preferably,the cost-effectiveness model comprises a Markov model, differentialequations or difference equations.

In some embodiments, the step of processing the information to determinea threshold value or range of values for results of the test furthercomprises determining the threshold value or range of values thatmaximize the net health benefits; and setting the threshold value orrange of values as the threshold value or range of values that maximizethe net health benefits.

The method can further comprise the step of determining a plurality ofestimates of the threshold value or range of values, and wherein thestep of processing can comprise solving the cost effectiveness model foreach of the plurality of estimates.

In some embodiments, the step of processing the information to determinea threshold value or range of values for results of the test comprisesdetermining a relationship between values for the test and the amount ofpatients having values for the test at or below that value.

Furthermore, the step of processing the information can comprisedetermining a threshold value or range of values for results of the testfor respective groups of patients in the plurality of patients, whereinthe respective groups of patients differ from each other in one or moreof age, gender, clinical condition, hospital and geographical area.

In further embodiments, the method further comprises the steps ofreceiving a result for the test for a patient; comparing the result ofthe test to the determined threshold value or range of values; andoutputting a recommended treatment level for the patient based on theresult of the step of comparing.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to show more clearlyhow it may be carried into effect, reference will now be made, by way ofexample only, to the accompanying drawings, in which:

FIG. 1 is a block diagram of a system according to an embodiment of theinvention;

FIG. 2 is a table illustrating exemplary information that can be storedin the patient information database;

FIG. 3 is a table illustrating exemplary cost information that can bestored in the intervention/treatment database;

FIG. 4 is a flow chart illustrating a method according to an embodimentof the invention;

FIG. 5 illustrates exemplary relationships between test values forNTproBNP and health and financial outcomes;

FIG. 6 is a cumulative histogram showing the number of patients having acertain test value for NTproBNP;

FIG. 7 is a graph illustrating a relationship between test values forNTproBNP and quality of life;

FIG. 8 is a graph illustrating the calculated net health benefits as afunction of test values for NTproBNP;

FIG. 9 is a contour plot of net health benefits as a function of twothreshold values; and

FIG. 10 is a screenshot of an application providing a recommendation fora treatment level according to the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As indicated above, it is an object of the invention to provide a systemand method that can determine cut-off or threshold values for the testor tests which can be used to indicate the tier of service of thedisease management or treatment program that is most cost-effective forthe patient. The problem is how to determine the cut-off or thresholdvalue of the test result at which one particular intensity treatmentlevel of a chronic disease management program becomes morecost-effective than another one.

The most appropriate cut-off or threshold values are likely to vary byhospital/region/country due to a number of factors, including, but notlimited to, variations in the range of chronic disease management (CDM)programs available and the different levels of intensity of thetreatment offered; variations in the effect of the treatment level onpatient survival and use of care resources; variations in patientcase-mix, age and gender; variations in the perceived quality of lifefor different severity levels of the disease or condition; variations inthe amount of money that society is willing to pay for one(quality-adjusted) life year gained by the treatment level; variationsin patient motivational aspects, including knowledge of the disease, andpersonal preferences for intensity of the treatment administered as partof the chronic disease management program; and variations in patientability to adhere to the prescribed program level.

A treatment program provides care to individuals diagnosed with acertain disease, such as heart failure, diabetes, cancer, or kidneyfailure, and who are in need of treatment. A telemonitoring program forheart failure patients is an example of a treatment program where thelevels of service can follow a tiered approach. In such a telemonitoringprogram, one tier for patients at highest risk could comprise thecombination of vital sign monitoring, tailored coaching and education,as well as weekly telephone contact with a heart failure nurse. Anotherlevel for patients at medium risk would comprise coaching and educationand weekly nurse telephone contact, but not include daily weight andblood pressure measurements. Finally, a tier for patients at low riskwould comprise only weekly telephone support.

An exemplary system for determining cut-off or threshold valuesaccording to the invention is shown in FIG. 2. The system 2 comprises apatient information database 4 that stores or holds information onpatients, such as patient health records, that indicates details of thepatient, such as any of age, gender, ethnicity, current/previous medicalconditions, test results obtained using home, clinical or laboratorytests, the treatment program used to treat the medical condition(s)(including the intensity levels of the treatment program used) and/orthe outcome of the treatment program or the particular level oftreatment administered (e.g. patient recovery, patient survival time,whether hospitalization was required, etc.). The information stored inthe patient information database 4 may be hospital specific (i.e. onlyrelates to patients of a specific hospital or health care provider)and/or medical condition specific (i.e. only relating to patients withone or more specified medical conditions).

Exemplary information that can be stored in the patient informationdatabase 4 is shown in FIG. 2. Thus, in this example, the patientinformation database 4 stores information on patients with chronic heartfailure, and the information for each patient includes their date ofbirth, date of enrollment into the chronic heart failure treatmentprogram, the test results at the date of enrollment (in particular theNTproBNP level), the level of treatment provided to the patient(presented in terms of level I or II, with II being more intensive thanlevel I), the dates of hospital admission and the date of death of thepatient.

It will be appreciated that the information stored in the patientinformation database 4 will be information that is relevant to thecondition or disease of the patient. Thus, where the invention isapplied to, for example, patients with diabetes, information on glycatedhaemoglobin (HbAlc), which is a blood marker, can be stored in thepatient information database 4, and for which the invention could beused to determine threshold values. Likewise, for patients with chronickidney disease, the level of serum creatinine could be stored in thepatient information database 4, and appropriate thresholds for thismeasurement determined using the invention.

The information held in the patient information database 4 can beobtained from an existing hospital patient information database, or, ifno existing database is available for a particular medical condition (orif sufficient information for a particular condition is not available),‘default’ values can be obtained and input to the database 4 from, forexample, literature studies.

The system 2 also comprises an intervention/treatment program database 6that includes information on the treatment programs available for therelevant medical conditions. The information can indicate the differentlevels of intensity of the treatment program that are available, themedications (including the amounts and frequency) to be administered ateach level, the test(s) required to be undertaken on or by the patient,whether hospital admission is required and/or whether surgicalintervention is required. The intervention/treatment program database 6also comprises information on the cost of the patient undergoing eachintensity level of the treatment program. Exemplary information for atreatment program for chronic heart failure is shown in FIG. 3. In thisexample, the cost information includes the cost per year of the patientundergoing the different intensity levels of treatment, the cost of anyhospitalization required for the patient, the time span of interest, thewillingness to pay for one quality-adjusted life year, the discount ratecost and the discount rate effect.

The ‘willingness to pay’ is the monetary equivalent to aquality-adjusted life year, i.e. the amount of money which a person orsociety is willing to pay to gain one additional life year for thepatient with maximum quality of life. Regarding the discount rate costand discount rate effect, in general, future costs and health effectsshould be weighted less heavily than present ones in acost-effectiveness analysis. Discounting is a process for computing howmuch resource costs or health effects at some point in the future areworth today.

It will be appreciated that although the patient information database 4and intervention/treatment database 6 are shown as separate elements ofthe system 2, they can be implemented as a single database or in asingle database element of the system 2.

The databases 4, 6 are connected to a processing module 8 that is ableto retrieve the information stored in the databases 4, 6 and performprocessing on that information to determine the threshold values for thetests used to assess the prognosis or current status of the medicalcondition according to the invention. These thresholds can then be usedby the system 2 or by a care provider (such as a physician) or thepatient to select the most cost effective treatment level to be providedto the patient.

The system 2 also comprises a user interface 10 that allows a user ofthe system 2 (which may be the patient, a care provider and/or systemadministrator) to input information into the system 2 (including theinformation to be stored in the databases 4, 6) and to control operationof the system 2. The user interface 10 can comprise user inputs (such asa keyboard, keypad, mouse, touch screen, etc.) and a display.

In embodiments where the system 2 can provide recommendations for themost cost-effective treatment level to be provided to the patient or toassign the patient to the most cost-effective treatment level, the userinterface 10 can allow the patient or care provider to input a measuredvalue for the appropriate test. The recommendation can then be presentedto the patient or care provider via the user interface 10.

The flow chart in FIG. 4 illustrates a method of determining thresholdvalues or a range of values for a test for use in assigning patients tothe most cost-effective level of treatment for a clinical condition. Themethod is performed by the processing module 8. Briefly, althoughmethods are available for performing a cost-utility analysis ofpharmaceutical interventions (e.g. in US 2007/0179809), these methods donot take account of the fact that results of prognostic laboratory orhome tests can be correlated with or be predictors of health effects(mortality, hospitalization and quality of life). In the case of heartfailure, a patient's NTproBNP levels can be related to mortality andhealth care resource usage. The current invention recognizes this anduses historical patient information to derive relationships between testvalues and the outcome of the treatment for the patient. Theserelationships are then used with cost information to derive thresholdvalues for the tests that allow the most cost effective treatment levelto be selected for a patient.

In step 101, information on a plurality of patients having a particularclinical condition is obtained. The information includes values for atest (for example a home or laboratory test) performed on the patients,information on the treatment level for the clinical condition providedto the patient and information on the outcome of the clinical conditionfor the patient. This information is obtained by the processing module 8from the patient information database 4.

In step 103, the processing module 8 obtains information on the costassociated with providing the different levels of treatment to thepatient from the intervention/treatment database 6.

Then, in step 105, the processing module 8 processes the information onthe plurality of patients to determine a relationship between values forthe test used to assess the current or initial status of the clinicalcondition and the health outcome of the clinical condition for thepatient for each of the possible treatment levels of the treatmentprogram. The outcome of the clinical condition for the patient can bemeasured, for example, in terms of the 1-year mortality rate, and/or theperceived quality of life. FIG. 5( a) shows a relationship betweenNTproBNP levels and health outcome (measured in terms of the probabilityof dying within 1 year of enrollment into the program) derived from datafor patients with chronic heart failure for two different treatmentlevels (program level I and program level II). These relationships canbe obtained from the information in the patient information database 4by, for example, using classical statistical techniques such as linearor logistic regression of the test values on the selected outcome value.Those skilled in the art will be aware of alternative techniques thatcan be used to determine these relationships.

In step 107, the processing module 8 processes the information on theplurality of patients and the cost information to determine arelationship between values for the test used to assess the current orinitial status of the clinical condition and the cost outcome ofproviding each level of treatment for the clinical condition for thepatient. FIG. 5( b) shows a relationship between NTproBNP levels andcost outcome (measured in terms of the probability of being hospitalizedwithin 1 year of enrollment into the program) derived from data forpatients with chronic heart failure for two different treatment levels(program level I and program level II). These relationships can beobtained from the information in the patient information database 4 andinformation in the intervention/treatment database 6 by, for example,using linear or logistic regression of the test values on the selectedcost outcome value. Again, those skilled in the art will be aware ofalternative techniques that can be used to determine theserelationships.

These relationships are used in step 109 to determine a threshold valueor range of values for each of the different levels of treatment, withthe threshold value or range of values for each treatment level beingset to provide the most cost effective treatment level for a patienthaving a given result for the test. The threshold value or range ofvalues for each treatment level are then output by the processing module8 for use by a patient or health care professional in determining alevel of treatment to be provided to the patient.

Briefly, in a preferred embodiment of step 109, one or more initialestimates for the cut-off or threshold value are made for each treatmentlevel, a cost-effectiveness model (e.g., a Markov model) is simulated toestablish the net health benefits of the patient population underinvestigation. Net health benefits are defined as the accumulated effectof the applied level of the treatment program over a given time span(measured in quality adjusted life years), from which the totalaccumulated costs divided by the willingness-to-pay are subtracted. Asensitivity analysis of the cut-off value can then be performed toobtain the cut-off value at which the net health benefits are maximized.Alternatively, the optimal cut-off value can be obtained byincorporating a numerical optimization algorithm within thecost-effectiveness analysis. The latter may reduce computation time ifnot one but multiple cut-off values are to be found (e.g. in case thatthere are more than two intensity levels for the chronic diseasemanagement program).

In the following paragraphs, a preferred implementation of step 109 willbe described in more detail. In particular embodiments, once therelationships between test values and health outcome (step 105) and testvalues and cost outcome (step 107) have been derived, a relationshipbetween test values and the fraction or percentage of patients having atest result at or below that value is also derived. This relationshipcan be derived by generating an (interpolated) cumulative histogram fromthe information in the patient information database 4, an example ofwhich can be seen in FIG. 6.

Confounders such as age and gender can also be taken into account in themethod according to the invention by determining the test threshold(s)using the approach outlined in this invention for subgroups of patients.For example, subgroups can be constructed by only considering male orfemale patients in the age range 40 through 60, 60 through 80 or 80through 100, and the relationships specified in steps 105 and 107 can bederived for each subgroup. Step 109 can then be performed for eachsubgroup to determine the subgroup-appropriate threshold(s).

In some embodiments, thresholds for multiple types of tests can beobtained, for instance, to evaluate disease severity by assessingco-morbidities as well (for example creatinine levels for kidney failurein heart failure patients). In the case of multiple tests, multivariablerelations need to be formulated. For example, the relationship betweenoutcome and NT-proBNP and creatinine would be described by a surfaceinstead of by a single line as is the case for NT-proBNP alone.

In an optional step, a relationship between test value and a normalizedquality of life measure between 0.0 and 1.0 (e.g., perceived quality oflife for patients with a certain test value) could be determined by theprocessing module 8 based on information from patients or informed usersand/or from information in the medical literature. If such quality oflife data is not provided or otherwise available, it can be assumed thatall patients have the maximum quality of life equal to 1.0. An exemplaryrelationship between test results and quality of life is shown in FIG.7.

Next, a cost-effectiveness model is defined. This can be in the form ofa Markov model, differential equations, difference equations or anothermethod. In the preferred embodiment described further below, thecost-effectiveness model is in the form of differential equations.

In particular, if the fraction of patients enrolled in a certaintreatment program level i is y_(i), then the rate of change of thatfraction (dy_(i)/dt) depends on the average mortality rate r_(die,i)among patients in program level I as follows:

$\begin{matrix}{\frac{y_{i}}{t} = {{- r_{{die},i}}{y_{i}\left( {{i = 1},2} \right)}}} & (1)\end{matrix}$

It is assumed that patients do not move from one treatment level toanother, but remain within the same program level for the time span overwhich the future net health benefits are calculated.

The cumulative health effects E (expressed in quality adjusted lifeyears) can then be computed as:

$\begin{matrix}{\frac{E}{t} = {\frac{1}{\left( {1 + r_{E}} \right)^{t}}{\sum\limits_{i = 1}^{2}{q_{i}y_{i}}}}} & (2)\end{matrix}$

where r_(E) is the discount rate for effects and q_(i) are weights forthe quality of life for patients in program level i. The cumulativecosts C can be further computed from the average hospitalization costsc_(hosp), the average hospitalization rate for each program levelr_(hosp,i) and the cost c_(prog,i) for each program level i:

$\begin{matrix}{\frac{C}{t} = {\frac{1}{\left( {1 + r_{C}} \right)^{t}}{\sum\limits_{i = 1}^{2}{\left( {{c_{hosp}r_{{hosp},i}} + c_{{prog},i}} \right)y_{i}}}}} & (3)\end{matrix}$

where r_(C) is the discount rate for costs.

Solving the cost-effectiveness model can be achieved by solvingdifferential equations (1), (2) and (3) by using numerical methods(e.g., Euler method or Runge-Kutta method), which are known to theskilled person. Solving the differential equations requires initialvalues for y_(i) as well as a time span over which the accumulatedhealth effects and costs are computed. Upon solving thecost-effectiveness model, the accumulated health effects E and costs Cover the given time span are determined. The net health benefits (NHB)associated with the costs and effects are given by:

$\begin{matrix}{{NHB} = {E - \frac{C}{WTP}}} & (4)\end{matrix}$

where E is the total health effect (in quality adjusted life years), Cis the total cost, and WTP is the willingness to pay (e.g., societalwillingness to pay for one quality adjusted life year).

The test threshold value which results in the maximum net healthbenefits (NHB) can then be found by simulating the cost-effectivenessmodel for a range of test threshold values. This is achieved by solvingthe differential equations (1), (2) and (3) multiple times, namely formultiple values of the test threshold. From the resulting values for theeffect E and the cost C at the end of the time span, the net healthbenefits can then be computed using equation (4). The latter is alsodone for the multiple values of the test threshold. This results in arelationship between the net health benefits and the test threshold.FIG. 8 shows the net health benefits as a function of threshold valuesfor the NTproBNP level. It is assumed that patients having a test resultbelow the threshold of the test value will be in treatment level I andpatients having a test result above the threshold will be in level II.This example only considers two treatment levels and one test thresholdvalue.

In this example, the maximum net health benefit level was found at aNTproBNP level of 7800. This value of NTproBNP, where the maximum nethealth benefits are achieved, can then be set as the cut-off orthreshold value above/below which patients are treated with a differenttreatment level.

It should be noted that the NHB are maximized here for the populationunder consideration (e.g., the population of typical heart failurepatients that present at the hospital or area where the invention isused), not for an individual patient. To achieve maximum NHB for thepatient population in the current example, all patients below the testthreshold value should be enrolled in program level I, and all patientsabove the threshold should be enrolled in program level II.

It should be noted that the variables r_(die,i), r_(hosp,i), and q_(i)as well as the initial values for solving y_(i) (y_(i,0)) for eachtreatment level are dependent on the fraction of patients having a testvalue above or below the cut-off value. The initial values y_(i,0) areidentical to the fraction of patients within each range. The averagerates are computed using the distribution of patients with a particulartest result. For example, for two program levels (i=1,2) with testthreshold Th, the mortality rates for the two program levels can becomputed using the one-year probability of mortality p_(die) as follows:

$\begin{matrix}{p_{{die},1} = \frac{\int_{0}^{Th}{{p_{die}({test})}{N({test})}\ {{test}}}}{\int_{0}^{Th}{{N({test})}\ {{test}}}}} & (5) \\{p_{{die},2} = \frac{\int_{Th}^{\infty}{{p_{die}({test})}{N({test})}\ {{test}}}}{\int_{Th}^{\infty}{{N({test})}\ {{test}}}}} & (6)\end{matrix}$

Here p_(die) and the fraction of patients N with certain NTproBNP levelare functions of the test value. The probability can be converted to arate using

$\begin{matrix}{r = {\frac{1}{t}{\ln \left( {1 - p} \right)}}} & (7)\end{matrix}$

with dt the time span of interest (e.g., one year). In a similarfashion, average hospitalization rates and quality of life for eachprogram level can be computed.

If multiple cut-off values need to be defined (e.g., two thresholds inthe case where there are three levels of intensity in the treatmentprogram), the net health benefits can be computed as a function of themultiple threshold values in order to be able to compute the maximum nethealth benefits.

In the case of M treatment levels, the equations (1)-(3) and (5)-(6)above can be generalized to:

$\begin{matrix}{\frac{y_{i}}{t} = {{- r_{{die},i}}{y_{i}\left( {{i = 1},\ldots \mspace{14mu},M} \right)}}} & (8) \\{\frac{E}{t} = {\frac{1}{\left( {1 + r_{E}} \right)^{t}}{\sum\limits_{i = 1}^{M}{q_{i}y_{i}}}}} & (9) \\{\frac{C}{t} = {\frac{1}{\left( {1 + r_{C}} \right)^{t}}{\sum\limits_{i = 1}^{M}{\left( {{c_{hosp}r_{{hosp},i}} + c_{{prog},i}} \right)y_{i}}}}} & (10) \\{p_{{die},i} = \frac{\int_{{Thi} - 1}^{Thi}{{p_{die}({test})}{N({test})}\ {{test}}}}{\int_{{Thi} - 1}^{Thi}{{N({test})}\ {{test}}}}} & (11)\end{matrix}$

where Thi and Thi−1 for a particular treatment level are determinedaccording to the position of the treatment level in the hierarchy oftreatment levels. For example, in the case of three treatment levels,the three versions of equation (11) will be:

$\begin{matrix}{p_{{die},1} = \frac{\int_{0}^{{Th}\; 1}{{p_{die}({test})}{N({test})}\ {{test}}}}{\int_{0}^{{Th}\; 1}{{N({test})}\ {{test}}}}} & (12) \\{p_{{die},2} = \frac{\int_{{Th}\; 1}^{{Th}\; 2}{{p_{die}({test})}{N({test})}\ {{test}}}}{\int_{{Th}\; 1}^{{Th}\; 2}{{N({test})}\ {{test}}}}} & (13) \\{p_{{die},3} = \frac{\int_{{Th}\; 2}^{\infty}{{p_{die}({test})}{N({test})}\ {{test}}}}{\int_{{Th}\; 2}^{\infty}{{N({test})}\ {{test}}}}} & (14)\end{matrix}$

In equations (12)-(14), Th1 and Th2 are the thresholds for thetransition from treatment level I to II and treatment level II to IIrespectively. Furthermore, in solving the differential equations andmaximizing the net health benefits (NHB), it should be ensured thatTh2>Th1.

A contour plot showing net health benefits as a function of the twothreshold values is shown in FIG. 9. It can be seen that the maximum nethealth benefits occur at the point marked ‘x’ with a value ofapproximately 1×10⁴ for the first threshold (i.e. the threshold betweenlevels 1 and 2) and a value of approximately 1.8×10⁴ for the secondthreshold (i.e. the threshold between levels 2 and 3). Alternatively, anoptimization algorithm can be implemented (for example aLevenberg-Marquardt algorithm) to compute the local maximum of NHBfaster. In the latter case, the differential equations are solved forinitial estimates of Th1 and Th2, which are then adapted in a “smart”fashion to maximize NHB and to arrive at the optimal threshold levelswithout needing to calculate the NHB for many combinations of Th1 andTh2, (i.e. it is calculated only for a few combinations). There arevarious optimization techniques known to the skilled person to achievethis goal.

It will be appreciated that constraints on the possible threshold valuescan be applied where there are more than two intensity levels oftreatment. For example, in the case of three intensity levels (i.e. twothresholds), one of the test thresholds should be higher than the othertest threshold.

Furthermore, confidence intervals for the derived threshold value can beestimated by defining distributions of the input parameters (e.g.,one-year survival for each intensity level). These distributions can beachieved by defining them as known parametric distributions (e.g., betadistribution for probabilities), or by bootstrapping the local(hospital) data set. Monte-Carlo simulations of the cost-effectivenessmodel can then be employed to obtain a distribution (and correspondingconfidence interval) of the net health benefits and the estimatedthreshold values.

In case no local data is available to describe the relations in thedata, predefined functions can be provided to the data processing system(i.e. the relationships in steps 105 and 107 are predefined). Thesefunctions can then be replaced (i.e. steps 105 and 107 repeated) bylocal relationships once sufficient data is available.

As indicated above, the treatment levels available to a patient as wellas the outcomes for the patient and the financial cost may vary byhospital or zip code. As a result, the system 2 can determine thresholdvalues that are valid for a particular hospital or area, oralternatively respective threshold values for multiple hospitals andareas (where the database 4 and 6 contains sufficient information).

In some embodiments, general relationships between the test value ofinterest and clinical outcomes for each treatment program level can becomputed (or derived from the literature) and validated prior toexecution of the method in FIG. 4, which means steps 105 and 107 in FIG.4 comprise adapting or calibrating the general relationships to thespecific local patient data stored in the patient information database 4and treatment/intervention database 6. Different regions may come with adifferent case-mix (e.g., a higher prevalence of the disease, differentage/gender distribution, etc.). Calibration accounts for these issues byadjusting the relationships.

It will be appreciated that the information in the patient informationdatabase 4 can be updated or added to over time, which means that steps105-109 can be repeated using the current information in the database 4on a regular basis to update the threshold values.

Once the threshold(s) or range of values for each treatment level havebeen derived according to the invention, they can be used to assignpatients to the most cost effective treatment level. In particular, ahealth care professional can compare a patient's test results with thederived threshold value(s) and read-off the appropriate treatment levelfor the patient. Alternatively, this process can be automated, with thepatient or health care professional inputting the test results into acomputer (for example the system 2 or a separate computer system) andthe computer outputting the suggested treatment level (alternatively thetest result can be supplied directly from the laboratory or homeequipment to the computer). The suggested treatment level can bereviewed for suitability by the health care professional in view of thepatient's other symptoms and general health condition.

An exemplary user interface 200 allowing manual input of the testresults into the computer is shown in FIG. 10. This user interface 200could be provided by system 2 (i.e. user interface 10) or by a separatecomputer (including a mobile telephone, smart phone or tablet computer).The user interface 200 includes fields for various pieces of patientspecific information such as name, age, gender, body mass index (BMI),blood pressure, urea test result and patient-indicated quality of life.The interface 200 also includes a field for the NTproBNP test result tobe entered. Once this test result is input, a recommendation for themost cost effective level of treatment is shown on the interface 200. Inthe illustrated example, the recommended treatment level is ‘MotivaGuide’, rather than ‘Motiva Coach’, ‘Motiva Monitor’ or ‘Usual Care’.The graph provided on the interface 200 also shows the annual hospitalcosts for each level of treatment.

There is therefore provided a system and method for determiningthreshold values or a range of values for a test used to assess thecurrent condition of the patient, with the threshold values or range ofvalues being set to indicate the most cost effective intensity level oftreatment for the patient having a particular test value.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive; theinvention is not limited to the disclosed embodiments.

Variations to the disclosed embodiments can be understood and effectedby those skilled in the art in practicing the claimed invention, from astudy of the drawings, the disclosure and the appended claims. In theclaims, the word “comprising” does not exclude other elements or steps,and the indefinite article “a” or “an” does not exclude a plurality. Asingle processor or other unit may fulfill the functions of severalitems recited in the claims. The mere fact that certain measures arerecited in mutually different dependent claims does not indicate that acombination of these measures cannot be used to advantage. A computerprogram may be stored/distributed on a suitable medium, such as anoptical storage medium or a solid-state medium supplied together with oras part of other hardware, but may also be distributed in other forms,such as via the Internet or other wired or wireless telecommunicationsystems. Any reference signs in the claims should not be construed aslimiting the scope.

1. A system for determining a threshold value or range of values forresults of a test used to assign patients to a particular level oftreatment for a clinical condition, the system comprising: one or moredatabases storing historical information on a plurality of patientshaving the clinical condition, the information including values for thetest performed on the patients, information on the treatment level forthe clinical condition provided to the patients, there being a pluralityof available treatment levels for the clinical condition, information onthe outcome of the clinical condition for the patients and informationon the cost associated with providing each of the treatment levels tothe patients; and a processing module in communication with said one ormore databases, the processing module determining a threshold value orrange of values for results of the test from the information in said oneor more databases, with the threshold value or range of valuesindicating the most cost effective treatment level for a given testresult.
 2. The system of claim 1, wherein the processing module isconfigured to determine a threshold value or range of values for resultsof the test from the information by: determining a relationship betweenvalues for the test and the health outcome of the clinical condition foreach treatment level.
 3. The system of claim 2, wherein the processingmodule is configured to determine the relationship through statisticalanalysis of the information stored in the one or more databases.
 4. Thesystem of claim 1, wherein the processing module is configured todetermine a threshold value or range of values for results of the testfrom the information by: determining a relationship between values forthe test and the monetary cost outcome for each treatment level.
 5. Thesystem of claim 4, wherein the processing module is configured todetermine the relationship through statistical analysis of theinformation stored in the one or more databases.
 6. The system of claim1, wherein the processing module is configured to determine a thresholdvalue or range of values for results of the test from the informationby: determining a relationship between net health benefits and thresholdvalues or ranges of values.
 7. The system of claim 6, wherein the nethealth benefits are the accumulated effect of said treatment level overa predefined time period minus the total accumulated monetary costdivided by willingness to pay, the willingness to pay being the monetaryequivalent to a quality-adjusted life year.
 8. The system of claim 6,wherein the processing module is configured to determine therelationship between net health benefits and threshold values or rangesof values by: solving a cost-effectiveness model for each treatmentlevel to determine net health benefits for the plurality of patients. 9.The system of claim 8, wherein the cost-effectiveness model comprises aMarkov model, differential equations or difference equations.
 10. Thesystem of claim 8, wherein the processing module is further configuredto determine a threshold value or range of values for results of thetest from the information by: determining the threshold value or rangeof values that maximize the net health benefits; and setting thethreshold value or range of values as the threshold value or range ofvalues that maximize the net health benefits.
 11. The system of claim 8,wherein the processing module is configured to determine a plurality ofestimates of the threshold value or range of values and to solve thecost effectiveness model for each of the plurality of estimates.
 12. Thesystem of claim 1, wherein the processing module is configured todetermine a threshold value or range of values for results of the testfrom the information by: determining a relationship between values forthe test and the amount of patients having values for the test at orbelow that value.
 13. The system of claim 1, wherein the processingmodule is configured to determine a threshold value or range of valuesfor results of the test for respective groups of patients in theplurality of patients, wherein the respective groups of patients differfrom each other in one or more of age, gender, clinical condition,hospital and geographical area.
 14. The system of claim 1, wherein theprocessing module is further configured to receive a result for the testfor a patient, compare the result of the test to the determinedthreshold value or range of values, and to output a recommendedtreatment level for the patient based on the result of the comparison.15. A method of determining a threshold value or range of values forresults of a test used to assign patients to a particular level oftreatment for a clinical condition, the method comprising: obtaininghistorical information on a plurality of patients having the clinicalcondition, the information including values for the test performed onthe patients, information on the treatment level for the clinicalcondition provided to the patients, there being a plurality of availabletreatment levels for the clinical condition, and information on theoutcome of the clinical condition for the patients; obtaininginformation on the cost associated with providing each of the treatmentlevels to the patients; processing the information to determine athreshold value or range of values for results of the test, thethreshold value or range of values indicating the most cost effectivetreatment level for a given test result.
 16. The method of claim 15,wherein the step of processing the information to determine a thresholdvalue or range of values for results of the test comprises: determininga relationship between values for the test and the health outcome of theclinical condition for each treatment level.
 17. The method of claim 16,wherein the step of determining the relationship comprises statisticallyanalyzing the obtained information.
 18. The method of claim 15, whereinthe step of processing the information to determine a threshold value orrange of values for results of the test comprises: determining arelationship between values for the test and the monetary cost outcomefor each treatment level.
 19. The method of claim 18, wherein the stepof determining the relationship comprises statistically analyzing theobtained information.
 20. The method of claim 15, wherein the step ofprocessing the information to determine a threshold value or range ofvalues for results of the test comprises: determining a relationshipbetween net health benefits and threshold values or ranges of values.